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OnlineCyberTools MCP (280+ filterable tools)

data_table_generator

Read-onlyIdempotent

Renders tabular data as Markdown, HTML, CSV, or TSV tables, and parses those formats into structured headers and rows.

Instructions

Format And Parse Tabular Data Tables. Deterministically converts supplied tabular data (header row plus 2-D rows) into a Markdown (GFM), HTML, CSV (RFC 4180), or TSV table, and parses CSV/TSV/Markdown text back into structured headers and rows. Does NOT generate random or sample data: it only formats or parses the exact cells you pass. Use operation render to serialize headers/rows into one of the four output formats with optional per-column alignment; use operation parse to read a pasted table string into headers/rows. Use this instead of data_sample_data_generator or data_data_faker (which invent random records), and prefer it over webdev_csv_to_json / webdev_json_to_csv when you need Markdown/HTML output or alignment-aware Markdown rather than JSON. Stateless, read-only, offline pure-compute; no auth required; default anonymous rate limit 60 requests/minute. Returns the formatted table string plus normalized headers, rows, rowCount, and columnCount.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
operationNorender serializes headers/rows into a table string; parse reads a table string into headers/rows. Defaults to render.render
formatNoTarget/source format. For render one of markdown, html, csv, tsv. For parse only csv, tsv, or markdown are accepted (html is render-only). Required for both operations.
headersNorender only. Optional array of column header strings; max 200. When present and content rows exist, its length must equal the widest row or the request is rejected.
rowsNorender only. Array of rows, each an array of scalar cells (string, number, boolean, null); max 10000 rows and 200 columns per row. Required for render.
alignmentNorender only, optional. Per-column alignment for markdown and html output; each entry is left, center, or right. Padded with left to column count. Ignored for csv/tsv.
sourceNoparse only. The raw CSV, TSV, or Markdown table text to parse. The first row is treated as headers. Null or empty returns empty headers/rows. Required for parse.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoTrue when the operation completed without a validation error.
operationNoEcho of the requested operation (render or parse).
resultNorender returns format, headers, rows, output, rowCount, columnCount. parse returns headers, rows, rowCount, columnCount (no output/format).
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds behavioral context beyond annotations: stateless, read-only, offline pure-compute, no auth required, default rate limit 60/min, and deterministic behavior. Annotations declare readOnlyHint=true and idempotentHint=true, which the description corroborates. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is slightly verbose but well-structured: it starts with the main purpose, then lists capabilities, then exclusions, then usage guidance, then behavioral traits. Every sentence adds value, but it could be more concise by separating the guidance into bullet points.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists (context indicates true), the description covers all necessary aspects: operations, formats, behavioral notes, auth, rate limits, and disambiguation from siblings. No gaps for a table formatting/parsing tool with 6 parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the two operations ('render' and 'parse') and noting the purpose of parameters like alignment (per-column) and the distinction between operation requirements. While not fully compensating for missing schema descriptions (none needed), it provides helpful operational context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it formats and parses tabular data, specifies supported formats (Markdown, HTML, CSV, TSV), and explicitly distinguishes from sibling tools like data_sample_data_generator and webdev_csv_to_json. The verb+resource combination is specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance: 'Use this instead of data_sample_data_generator or data_data_faker (which invent random records), and prefer it over webdev_csv_to_json / webdev_json_to_csv when you need Markdown/HTML output or alignment-aware Markdown rather than JSON.' It also states what the tool does not do ('Does NOT generate random or sample data').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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